{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "**任务**\n",
    "- 基于usa_housing_price.csv数据，建立线性回归模型，并预测美国不同地区房价。\n",
    "- 1、以面积为输入变量，建立单因子模型，评估模型表现，可视化线性回归预测结果\n",
    "- 2、以income,house age,numbers of rooms,population,area为输入变量，建立多因子模型，评估模型表现，可视化线性回归预测结果\n",
    "- 3、预测income=65000, House age=5, number of rooms=5, population=30000, size=200的合理房价"
   ],
   "id": "1cfcdcd28ecb3f64"
  },
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-06-15T09:56:52.575419Z",
     "start_time": "2025-06-15T09:56:51.946351Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-15T09:58:22.562011Z",
     "start_time": "2025-06-15T09:58:22.533705Z"
    }
   },
   "cell_type": "code",
   "source": [
    "data = pd.read_csv('data/usa_housing_price.csv')\n",
    "data.head()"
   ],
   "id": "bb58cd416a99eae2",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Avg_Area_Income  Avg_Area_House_Age  Avg_Area_Number_of_Rooms  \\\n",
       "0      79545.45857            5.682861                  7.009188   \n",
       "1      79248.64245            6.002900                  6.730821   \n",
       "2      61287.06718            5.865890                  8.512727   \n",
       "3      63345.24005            7.188236                  5.586729   \n",
       "4      59982.19723            5.040555                  7.839388   \n",
       "\n",
       "   Avg_Area_Number_of_Bedrooms  Area_Population         Price  \\\n",
       "0                         4.09      23086.80050  1.059034e+06   \n",
       "1                         3.09      40173.07217  1.505891e+06   \n",
       "2                         5.13      36882.15940  1.058988e+06   \n",
       "3                         3.26      34310.24283  1.260617e+06   \n",
       "4                         4.23      26354.10947  6.309435e+05   \n",
       "\n",
       "                                             Address  \n",
       "0  208 Michael Ferry Apt. 674 Laurabury, NE 37010...  \n",
       "1  188 Johnson Views Suite 079 Lake Kathleen, CA ...  \n",
       "2  9127 Elizabeth Stravenue Danieltown, WI 06482-...  \n",
       "3                           USS Barnett FPO AP 44820  \n",
       "4                          USNS Raymond FPO AE 09386  "
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Avg_Area_Income</th>\n",
       "      <th>Avg_Area_House_Age</th>\n",
       "      <th>Avg_Area_Number_of_Rooms</th>\n",
       "      <th>Avg_Area_Number_of_Bedrooms</th>\n",
       "      <th>Area_Population</th>\n",
       "      <th>Price</th>\n",
       "      <th>Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>79545.45857</td>\n",
       "      <td>5.682861</td>\n",
       "      <td>7.009188</td>\n",
       "      <td>4.09</td>\n",
       "      <td>23086.80050</td>\n",
       "      <td>1.059034e+06</td>\n",
       "      <td>208 Michael Ferry Apt. 674 Laurabury, NE 37010...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79248.64245</td>\n",
       "      <td>6.002900</td>\n",
       "      <td>6.730821</td>\n",
       "      <td>3.09</td>\n",
       "      <td>40173.07217</td>\n",
       "      <td>1.505891e+06</td>\n",
       "      <td>188 Johnson Views Suite 079 Lake Kathleen, CA ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61287.06718</td>\n",
       "      <td>5.865890</td>\n",
       "      <td>8.512727</td>\n",
       "      <td>5.13</td>\n",
       "      <td>36882.15940</td>\n",
       "      <td>1.058988e+06</td>\n",
       "      <td>9127 Elizabeth Stravenue Danieltown, WI 06482-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63345.24005</td>\n",
       "      <td>7.188236</td>\n",
       "      <td>5.586729</td>\n",
       "      <td>3.26</td>\n",
       "      <td>34310.24283</td>\n",
       "      <td>1.260617e+06</td>\n",
       "      <td>USS Barnett FPO AP 44820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59982.19723</td>\n",
       "      <td>5.040555</td>\n",
       "      <td>7.839388</td>\n",
       "      <td>4.23</td>\n",
       "      <td>26354.10947</td>\n",
       "      <td>6.309435e+05</td>\n",
       "      <td>USNS Raymond FPO AE 09386</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-15T10:02:33.949780Z",
     "start_time": "2025-06-15T10:02:32.450562Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from matplotlib import pyplot as plt\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "fig1 = plt.subplot(231)\n",
    "plt.scatter(data.loc[:,'Avg_Area_Income'], data.loc[:,'Price'])\n",
    "plt.title(\"Price VS Income\")\n",
    "\n",
    "fig2 = plt.subplot(232)\n",
    "plt.scatter(data.loc[:,'Avg_Area_House_Age'], data.loc[:,'Price'])\n",
    "plt.title(\"Price VS House_Age\")\n",
    "\n",
    "fig3 = plt.subplot(233)\n",
    "plt.scatter(data.loc[:,'Avg_Area_Number_of_Rooms'], data.loc[:,'Price'])\n",
    "plt.title(\"Price VS Number_of_Rooms\")\n",
    "\n",
    "fig3 = plt.subplot(234)\n",
    "plt.scatter(data.loc[:,'Area_Population'], data.loc[:,'Price'])\n",
    "plt.title(\"Price VS Area_Population\")\n",
    "\n",
    "fig3 = plt.subplot(235)\n",
    "plt.scatter(data.loc[:,'Avg_Area_Number_of_Bedrooms'], data.loc[:,'Price'])\n",
    "plt.title(\"Price VS Number_of_Bedrooms\")\n"
   ],
   "id": "30c5d1bd2a0a6ab0",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Price VS Number_of_Bedrooms')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x1000 with 5 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-15T10:05:52.302545Z",
     "start_time": "2025-06-15T10:05:52.293056Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# define X,y\n",
    "X = data.loc[:,['Avg_Area_Income']]\n",
    "y = data.loc[:,'Price']\n",
    "y.head()"
   ],
   "id": "74e9ec26db038807",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.059034e+06\n",
       "1    1.505891e+06\n",
       "2    1.058988e+06\n",
       "3    1.260617e+06\n",
       "4    6.309435e+05\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-15T10:07:18.931962Z",
     "start_time": "2025-06-15T10:07:18.925894Z"
    }
   },
   "cell_type": "code",
   "source": [
    "X = np.array(X)\n",
    "X = np.reshape(X,(-1,1))"
   ],
   "id": "7dbeae25a7afff92",
   "outputs": [],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-15T10:07:21.499063Z",
     "start_time": "2025-06-15T10:07:20.513588Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# set up the linear regression model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "LR1 = LinearRegression()\n",
    "LR1.fit(X,y)"
   ],
   "id": "24c975cddf0fbe8",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ],
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url();\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy_X',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">copy_X&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">tol&nbsp;</td>\n",
       "            <td class=\"value\">1e-06</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('positive',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">positive&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-15T10:08:19.762523Z",
     "start_time": "2025-06-15T10:08:19.750134Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# calculate the price from income\n",
    "y_predict_1 = LR1.predict(X)\n",
    "print(y_predict_1)"
   ],
   "id": "eb80a93edcbefe3",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      " 1139204.74214557 1250400.45596889 1173305.06512062  912728.88676457\n",
      " 1048446.84892944 1157943.53867802 1562067.28302838 1275180.54185833\n",
      " 1465318.87932454 1133840.42283356 1166356.85602514 1571069.3286123\n",
      " 1401165.44596096  983529.00640434 1550918.65557667 1371296.68539836\n",
      "  968076.00572116 1049633.49239376 1260612.33052057 1219771.09461263\n",
      " 1381538.76711201 1137445.65376755 1316515.68721407 1253052.07137872\n",
      " 1173527.31267673 2061961.55348902 1033936.24123286]\n"
     ]
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-15T10:09:06.494517Z",
     "start_time": "2025-06-15T10:09:06.484513Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.metrics import mean_squared_error,r2_score\n",
    "mean_squared_error_1 = mean_squared_error(y,y_predict_1)\n",
    "r2_score_1 = r2_score(y,y_predict_1)\n",
    "print(mean_squared_error_1,r2_score_1)"
   ],
   "id": "4a82ee69f0d58995",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70178701038.99843 0.4035418014581793\n"
     ]
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-15T10:10:28.797198Z",
     "start_time": "2025-06-15T10:10:28.658929Z"
    }
   },
   "cell_type": "code",
   "source": [
    "fig6 = plt.figure(figsize=(8,5))\n",
    "plt.scatter(X,y,color='blue')\n",
    "plt.plot(X,y_predict_1,color='red')\n",
    "plt.title(\"Price VS Income\")\n",
    "plt.show()"
   ],
   "id": "e5cddd72abcf2a5",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-15T10:13:47.220841Z",
     "start_time": "2025-06-15T10:13:47.205017Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# define X_muti,y\n",
    "X_muti = data.drop(columns=['Price','Address'])\n",
    "X_muti"
   ],
   "id": "e5e1bf74fd6ac3f5",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     Avg_Area_Income  Avg_Area_House_Age  Avg_Area_Number_of_Rooms  \\\n",
       "0        79545.45857            5.682861                  7.009188   \n",
       "1        79248.64245            6.002900                  6.730821   \n",
       "2        61287.06718            5.865890                  8.512727   \n",
       "3        63345.24005            7.188236                  5.586729   \n",
       "4        59982.19723            5.040555                  7.839388   \n",
       "..               ...                 ...                       ...   \n",
       "690      72263.21441            5.917227                  5.336457   \n",
       "691      69246.15092            5.763367                  6.492639   \n",
       "692      65465.53989            5.809592                  6.735937   \n",
       "693     107701.74840            7.143522                  8.518608   \n",
       "694      58829.37333            6.846645                  6.462523   \n",
       "\n",
       "     Avg_Area_Number_of_Bedrooms  Area_Population  \n",
       "0                           4.09      23086.80050  \n",
       "1                           3.09      40173.07217  \n",
       "2                           5.13      36882.15940  \n",
       "3                           3.26      34310.24283  \n",
       "4                           4.23      26354.10947  \n",
       "..                           ...              ...  \n",
       "690                         4.04      28428.30127  \n",
       "691                         2.24      35005.79155  \n",
       "692                         3.38      31297.52713  \n",
       "693                         3.29      37619.43993  \n",
       "694                         3.20      31236.58232  \n",
       "\n",
       "[695 rows x 5 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Avg_Area_Income</th>\n",
       "      <th>Avg_Area_House_Age</th>\n",
       "      <th>Avg_Area_Number_of_Rooms</th>\n",
       "      <th>Avg_Area_Number_of_Bedrooms</th>\n",
       "      <th>Area_Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>79545.45857</td>\n",
       "      <td>5.682861</td>\n",
       "      <td>7.009188</td>\n",
       "      <td>4.09</td>\n",
       "      <td>23086.80050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79248.64245</td>\n",
       "      <td>6.002900</td>\n",
       "      <td>6.730821</td>\n",
       "      <td>3.09</td>\n",
       "      <td>40173.07217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61287.06718</td>\n",
       "      <td>5.865890</td>\n",
       "      <td>8.512727</td>\n",
       "      <td>5.13</td>\n",
       "      <td>36882.15940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63345.24005</td>\n",
       "      <td>7.188236</td>\n",
       "      <td>5.586729</td>\n",
       "      <td>3.26</td>\n",
       "      <td>34310.24283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59982.19723</td>\n",
       "      <td>5.040555</td>\n",
       "      <td>7.839388</td>\n",
       "      <td>4.23</td>\n",
       "      <td>26354.10947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>690</th>\n",
       "      <td>72263.21441</td>\n",
       "      <td>5.917227</td>\n",
       "      <td>5.336457</td>\n",
       "      <td>4.04</td>\n",
       "      <td>28428.30127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691</th>\n",
       "      <td>69246.15092</td>\n",
       "      <td>5.763367</td>\n",
       "      <td>6.492639</td>\n",
       "      <td>2.24</td>\n",
       "      <td>35005.79155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692</th>\n",
       "      <td>65465.53989</td>\n",
       "      <td>5.809592</td>\n",
       "      <td>6.735937</td>\n",
       "      <td>3.38</td>\n",
       "      <td>31297.52713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693</th>\n",
       "      <td>107701.74840</td>\n",
       "      <td>7.143522</td>\n",
       "      <td>8.518608</td>\n",
       "      <td>3.29</td>\n",
       "      <td>37619.43993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>58829.37333</td>\n",
       "      <td>6.846645</td>\n",
       "      <td>6.462523</td>\n",
       "      <td>3.20</td>\n",
       "      <td>31236.58232</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>695 rows × 5 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 16
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-15T10:13:52.518571Z",
     "start_time": "2025-06-15T10:13:52.407407Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# set up 2nd liner model\n",
    "LR_muti = LinearRegression()\n",
    "# train the model\n",
    "LR_muti.fit(X_muti,y)"
   ],
   "id": "93fc13c73af92184",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ],
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url();\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy_X',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">copy_X&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">tol&nbsp;</td>\n",
       "            <td class=\"value\">1e-06</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('positive',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">positive&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-15T10:31:36.249657Z",
     "start_time": "2025-06-15T10:31:36.232894Z"
    }
   },
   "cell_type": "code",
   "source": [
    "y_predict_muti = LR_muti.predict(X_muti)\n",
    "print(y_predict_muti)"
   ],
   "id": "c9f399b9228fff96",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      " 1244810.10795375 1975258.58245326  904143.33685747 1502621.08738692\n",
      " 1908317.47515    1547352.89524153 1468198.11111825  659171.75452117\n",
      "  970870.08257888 1103157.89435985 1334613.47110763 1160598.95325736\n",
      "  872200.41640191 1231345.54429874 1141486.31435831 1176526.94539861\n",
      " 1153321.65452485 1008983.65228317  978311.29239017 1542484.20344249\n",
      " 1384988.47289437 1804125.78294693 1443899.14475099 1473672.3032862\n",
      " 1377317.31481055 1233164.2505329  1359525.11264909 1014808.68501368\n",
      " 1032786.14320237 1230960.19347828  737803.15266865 1247173.18198444\n",
      " 1470425.28535545 1469046.13095576 1023380.59265588 1180827.0297456\n",
      " 1019126.72165676 1248384.3849616  1839360.01604866 1086127.19760453\n",
      " 1763181.45713943 1410403.63385111 2165926.07874889  728586.10744446\n",
      " 1021677.16373845 1122814.72352868 1645627.15114872 1329510.43810751\n",
      " 1543108.14612774 1550911.54849876  620248.47683289 1347041.78568789\n",
      " 1191951.34215034 1259385.6014212  1532199.57807354 1449508.83524191\n",
      "  718904.50904899 1144511.07764496  442711.59125468 1659617.10909806\n",
      " 1660308.06187421 1184582.46775258 1281122.68516884 1779933.91552157\n",
      " 1145622.95211176 1081015.27905557 1140489.56700132 1866316.68956779\n",
      " 1328570.84256371 1311776.59815519 1240480.10035123 1151612.28812647\n",
      " 1505395.3085458  1206467.82029087 1057021.60287605 1045140.06433411\n",
      "  907589.46189014 1498107.52290142 1493592.87900445 1268095.14694546\n",
      " 1345911.18793632  896283.3294108   913493.01880566 1147382.17657648\n",
      " 1309533.88704976 1655429.450586   1720868.95089816 1784165.68601292\n",
      " 1169997.96051677 1382842.12259683 1208649.27916901 1368561.54940527\n",
      " 1363893.2112976  1202391.39339245 1357408.52072263 1018391.64915783\n",
      " 1751952.80991493 1207123.34579747  650132.3707605  1584869.15895496\n",
      " 1253628.80358961 1392848.71035692 1309502.01797772 1438763.1894737\n",
      " 1373689.54475268  730773.93409884 1563021.76905358 1014107.08417829\n",
      " 1343328.98188762 1114667.86431343  981954.05196859 1138414.51294161\n",
      " 1035860.43966385 2477378.65598938 1030914.55988194]\n"
     ]
    }
   ],
   "execution_count": 18
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-15T10:32:42.661931Z",
     "start_time": "2025-06-15T10:32:42.653901Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.metrics import mean_squared_error,r2_score\n",
    "mean_squared_error_muti = mean_squared_error(y,y_predict_muti)\n",
    "r2_score_muti = r2_score(y,y_predict_muti)\n",
    "print(mean_squared_error_muti,r2_score_muti)"
   ],
   "id": "6d5cb9c1a5999d0a",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9829093743.571451 0.9164612131488238\n"
     ]
    }
   ],
   "execution_count": 19
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-15T10:33:09.061197Z",
     "start_time": "2025-06-15T10:33:09.052639Z"
    }
   },
   "cell_type": "code",
   "source": "print(mean_squared_error_1,r2_score_1)",
   "id": "8365ac23b6fa9bd1",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70178701038.99843 0.4035418014581793\n"
     ]
    }
   ],
   "execution_count": 20
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-15T10:34:23.269942Z",
     "start_time": "2025-06-15T10:34:23.147780Z"
    }
   },
   "cell_type": "code",
   "source": [
    "fig7 = plt.figure(figsize=(8,5))\n",
    "plt.scatter(y,y_predict_muti,color='blue')\n",
    "plt.show()"
   ],
   "id": "5842219dea4ed48c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 21
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-15T10:34:41.075480Z",
     "start_time": "2025-06-15T10:34:40.932164Z"
    }
   },
   "cell_type": "code",
   "source": [
    "fig8 = plt.figure(figsize=(8,5))\n",
    "plt.scatter(y,y_predict_1,color='blue')\n",
    "plt.show()"
   ],
   "id": "721e3ed0dfe1a1aa",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 22
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-15T10:38:03.060950Z",
     "start_time": "2025-06-15T10:38:03.050588Z"
    }
   },
   "cell_type": "code",
   "source": [
    "X_test = pd.DataFrame({'Avg_Area_Income':[65000],'Avg_Area_House_Age':[5],'Avg_Area_Number_of_Rooms':[5],'Avg_Area_Number_of_Bedrooms':[4],'Area_Population':[30000]})\n",
    "print(X_test)"
   ],
   "id": "df4589c28e40de91",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Avg_Area_Income  Avg_Area_House_Age  Avg_Area_Number_of_Rooms  \\\n",
      "0            65000                   5                         5   \n",
      "\n",
      "   Avg_Area_Number_of_Bedrooms  Area_Population  \n",
      "0                            4            30000  \n"
     ]
    }
   ],
   "execution_count": 27
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-15T10:38:07.447286Z",
     "start_time": "2025-06-15T10:38:07.440022Z"
    }
   },
   "cell_type": "code",
   "source": [
    "y_test_predict = LR_muti.predict(X_test)\n",
    "print(y_test_predict)"
   ],
   "id": "fc614e4683d37b20",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[655684.70884764]\n"
     ]
    }
   ],
   "execution_count": 28
  }
 ],
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